Emotional Expressions Reconsidered by Barrett et al.

Limitations of the Study

The significance of emotional intelligence as a part of the general notion of emotional competence is critical in nursing. The ability to decipher the changes in a patient’s attitudes and emotional stage, as a whole, is critical in meeting the needs of the patient in question in a manner as expeditious and effective as possible (Miners, Côté, & Lievens, 2018). Therefore, building emotional competence should be regarded as an essential quality of a nursing expert. In their article, Barrett, Adolphs, Marsella, Martinez, and Pollak posit that the analysis of facial expression allows a nurse to gain the experience needed to develop impeccable emotional intelligence skills. Despite having several limitations linked to the methodology used for the analysis, specifically, the presence of uncertainty in locating common characteristics of the six basic emotions, the authors have managed to attain a substantial amount of reliability, generalizability, validity, and specificity in their study.

Much to their credit, the authors of the research devote an extensive part of their analysis to the discussion of limitations and biases with which their study is associated. For instance, one of the most contentious aspects of the analysis which is the subjectivity and culture-specific perception of emotions is addressed straightaway in the analysis. According to Barrett et al., “We do not discuss every emotion category ever studied in the science of emotion. We do not discuss the many emotion categories that exist in non-English-speaking cultures, such as gigil”. Indeed, due to the differences in the worldview, social standards for interactions, perception of self, and other aspects of developing cultural identity, people belonging to different cultural backgrounds may have an entirely different idea of emotions, their nature, and their exact number. In their research, Barrett et al. limit their analysis to happiness, sadness, fear, anger, disgust, and surprise, which are the six emotions that are typically defined as the main ones observed in people’s responses toward emotional stimuli (Karle et al., 2018). However, the researchers mention that the described emotions are not uniformly viewed as the cornerstone of emotional perception as a concept (Miners et al., 2018). Moreover, the authors correctly outline that the proposed way of viewing emotions may not coincide with the approach used in the cultures that are distanced from the traditional western philosophy. Therefore, the authors have defined the major limitation of the research that may have obscured the results of the analysis and made them not quite applicable to any cultural setting.

Another factor that limits the extent of the study results’ efficacy is the selection of the research method. In their exploration of emotions and the development of the platform for emotional intelligence acquisition, Barrett et al. deploy the qualitative method that allows delving into the nature of the phenomenon at hand, yet reduces the accuracy of the processed information. Consequently, the information retrieved for further analysis becomes quite vague and loses a substantial amount of objectivity.

The application of the computer software that allows creating models of facial expressions makes it possible to approximate the outcomes of the analysis and reduce the length of the study by substituting interactions with people with the application of a computer program. However, the identified framework is also quite flawed given the presence of high levels of generalization and the obvious absence of the inherent understanding of the human emotions in the software in question (Barrett et al., 2019). However, the opportunity to embrace and process vast masses of data within a very limited amount of time allows leveraging the described disadvantage. According to the authors, “This potential has not yet been exploited to explore the reliability and specificity in context-sensitive relations between facial movements and mental events” (Barrett et al., 2019, p. 49). Thus, the authors introduce an additional point of uncertainty into the assessment by including the software that may potentially affect the veracity of the research results.

Moreover, the fact that the researchers strived to address the methodological flaws of the study is also worth mentioning. By incorporating two measurement tools, Barrett et al. (2019) reduced the extent of the inaccuracy of the data retrieved for the analysis. Indeed, when combined, the Facial Action Coding System (FACS) and the use of automated algorithms in order to determine the unique characteristics of participants’ facial movements lead to a rise in research results’ accuracy.

Finally, the fact that the information concerning changes in the emotional state of the research participants was quite subjective needs to be discussed. Despite the attempt at increasing the objectivity of the data used for the analysis, Barrett et al. (2019) still had to rely on the descriptions of emotional states provided by the participants personally. The use of the information as it was delivered by the research participants led to an understandable drop in its veracity and introduced a tangible amount of personal perspective into the study.

Improvements and Next Steps

The presence of research biases in a study is practically inevitable since even in a nearly perfect setting, the presence of a small degree of uncertainty is unavoidable. However, with a set of tools and strategies aimed at reducing the threat of data misinterpretation and an increase in its credibility, a change in the extent of its subjectivity and the severity of limitations is possible. In the case under consideration, addressing the problem of personal perceptions can be addressed by reconsidering the researcher’s understanding of biasedness. Acknowledging the limitations of the analysis which Barrett et al. (2019) have already done in their paper, seems to be enough to improve the trustworthiness of research outcomes.

Since the paper involves the presentation of personal information and, more importantly, the inclusion of the data based on a personal evaluation of one’s emotional state, the study will inevitably contain personal opinions and rather subjective data. However, since the current approach toward the measurement of participants’ emotional state does not allow for a greater amount of precision, the problem at hand needs to be disregarded instead of being affected by the presence of an instrument that is expected to increase its veracity.

However, the specified step will imply dealing with significant complexities since the task of measuring emotions cannot be managed without a certain degree of subjectivity. Even if experts in physiology and the analysis of emotional responses are invited to participate in the study and determine the participants’ emotional state, the presence of personal opinions in the assessment will be inevitable (Fischer, Kret, & Broekens, 2018). Therefore, the described limitation is very difficult, if not impossible, to overcome. The acknowledgement of the problem, in turn, also does little for the actual credibility and accuracy of research outcomes.

The incorporation of a measurement scale that research participants could use to describe the strength of their emotion could be seen as a means of increasing the accuracy of research result. The described tool would also imply a significant amount of subjective data. However, it would help to structure the information obtained from patients and ensure that the correlation between a particular emotion shown by the people involved in the research (Matthews et al., 2015). Moreover, it would prove that the approach used to decipher it was strong enough to justify the application of the emotional intelligence technique in managing patients’ needs in the future.

Furthermore, the outcomes of the analysis imply that further steps will need to be taken in advancing the use of emotional intelligence in the selected domain. The integration of the ability to analyze facial expressions to elicit important information about the emotional state of a patient is critical in the scenarios that imply restrictions in nurse-patient communication. Thus, the approaches toward teaching nurses to apply appropriate techniques in examining changes in people’s facial expressions to denote their emotions and apply immediate and relevant measures are necessary.

The creation of teaching techniques, in turn, will have to be tested to gauge their efficacy as the tools for addressing patients’ needs. While the process of recognizing emotion based on the changes in a patient’s face may lead to quite accurate results, it is worth keeping in mind that it will have to take place under significant time constraints and, thus, will suggest the application of strategies for enhancing a nurse’s understanding of a patient’s needs (Clarke, Lovelock, & McNay, 2015). Therefore, the goals of follow-up studies will have to include the evaluation of effects that emotional intelligence approaches have on maintaining the well-being of inpatients. Similarly, tools and techniques for teaching nurse the basics of emotional intelligence and the methods of analyzing changes in patients’ expressions will have to be integrated into the learning framework. Consequently, a program for educating nurses will have to be designed and centered on the problems associated with the management of patients’ needs in the settings that involve significant time constraints.

Application of the Study

The outcomes of the analysis can be applied in the clinical context to determine the needs of people that are incapable of explaining their concerns to nurses and healthcare practitioners directly. Specifically, the approach based on emotional intelligence can be applied to managing their needs. The enhancement of neuroscience development as a field of studies that allows understanding how the nervous system works is one of the areas that are most likely to benefit from the results of the research performed by Barrett et al. The focus on the content-sensitive analysis of the process of experiencing emotions will help to boost the levels of neuroscience development, causing a vast shift in the treatment of the related issues and the development of the strategies for enhancing the functions of the nervous system.

Furthermore, the opportunities to decode the signals that indicate a change in one’s emotional state and, therefore, the actions that one is likely to take, will fuel the evolution of the artificial intelligence (AI) studies. The creation of neural networks and the effects of their performance will rise exponentially, causing a sizeable shift in the research that is currently taking place in the described domains (Dusseldorp, Guarin, van Veen, Jowett, & Hadlock, 2019). Allowing one to introduce the theory that embraces physiological, anatomical, and emotional performance of one’s body, neuroscience integrates the elements of biological and neurological perspectives,

On a larger scale, the concept of emotional intelligence as a part of a larger notion of emotional competence can be utilized to manage communication more effectively. For instance, the integration of the techniques based on emotional intelligence can be critical in predicting conflicts and preventing them from taking place, which is particularly important for multicultural and multidisciplinary groups. The problem of confrontations between the participants of multicultural and multidisciplinary groups is quite notorious in most settings, which include, but are not restricted to, nursing and healthcare, social work, or any workplace environment, in general (Eack, Mazefsky, & Minshew, 2015). While the increase in diversity rates is a crucial stage of encouraging a more active decision-making process and ensuring that the needs of all stakeholders are satisfied, it also suggests that different visions and perspectives will collide eventually. Thus, the introduction of emotional intelligence as the means of locating an emergent conflict and deescalating it should also be regarded as an important advantage that an emotional-intelligence-based strategy will give.

It is quite noteworthy that the potential of the research results is not restricted to the matters discussed above. While the described methods of utilizing the information derived from the article are the most obvious ones, there may be other ways of improving communication within a diverse environment. The results of the study can be applied to a business setting in order to address concerns that may arise after building a team of experts from different sociocultural backgrounds. Moreover, emotional intelligence as a general concept can be utilized in education to enhance the efficacy of the learning process and locate the situations in which students fail t understand the provided information. As a result, the problem of data misinterpretation will be addressed at its core, causing the process of learning to become more fruitful.


Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1-68. doi:10.1177/1529100619832930

Clarke, J., Lovelock, R., & McNay, M. (2015). Liberal arts and the development of emotional intelligence in social work education. The British Journal of Social Work, 46(3), 635-651. doi:10.1093/bjsw/bcu139

Dusseldorp, J. R., Guarin, D. L., van Veen, M. M., Jowett, N., & Hadlock, T. A. (2019). In the eye of the beholder: Changes in perceived emotion expression after smile reanimation. Plastic and Reconstructive Surgery, 144(2), 457-471. doi:10.1097/PRS.0000000000005865

Eack, S. M., Mazefsky, C. A., & Minshew, N. J. (2015). Misinterpretation of facial expressions of emotion in verbal adults with autism spectrum disorder. Autism, 19(3), 308-315. doi:10.1177/1362361314520755

Fischer, A. H., Kret, M. E., & Broekens, J. (2018). Gender differences in emotion perception and self-reported emotional intelligence: A test of the emotion sensitivity hypothesis. PloS One, 13(1), 1-19. doi:10.1371/journal.pone.0190712.

Karle, K. N., Ethofer, T., Jacob, H., Brück, C., Erb, M., Lotze, M.,… Kreifelts, B. (2018). Neurobiological correlates of emotional intelligence in voice and face perception networks. Social Cognitive and Affective Neuroscience, 13(2), 233-244. doi:10.1093/scan/nsy001

Matthews, G., Pérez-González, J. C., Fellner, A. N., Funke, G. J., Emo, A. K., Zeidner, M., & Roberts, R. D. (2015). Individual differences in facial emotion processing: Trait emotional intelligence, cognitive ability, or transient stress? Journal of Psychoeducational Assessment, 33(1), 68-82. doi:10.1016/j.paid.2018.02.017

Miners, C. T., Côté, S., & Lievens, F. (2018). Assessing the validity of emotional intelligence measures. Emotion Review, 10(1), 87-95. doi:10.1177/1754073917693688